# Human-AI Collaborative Autonomous Experimentation With Proxy Modeling for Comparative Observation

arXiv: https://arxiv.org/abs/2603.12618 · HTML: https://mlsanity.com/paper/2603.12618/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels](https://mlsanity.com/paper/2006.02489/) (2020)
- [Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning](https://mlsanity.com/paper/2111.14786/) (2021)
- [Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy](https://mlsanity.com/paper/2310.05018/) (2023)
- [Synergizing Human Expertise and AI Efficiency with Language Model for Microscopy Operation and Automated Experiment Design](https://mlsanity.com/paper/2401.13803/) (2024)
- [SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions](https://mlsanity.com/paper/2409.12295/) (2024)
- [Recent advances in the Bradley--Terry Model: theory, algorithms, and applications](https://mlsanity.com/paper/2601.14727/) (2026)
- [Estimation of partial rankings from sparse, noisy comparisons](https://mlsanity.com/paper/2501.02505/) (2025)
- [Human-AI collaborative autonomous synthesis with pulsed laser deposition for remote epitaxy](https://mlsanity.com/paper/2511.11558/) (2025)

## Built on by (most cited first, 1 of 1)

- [Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale](https://mlsanity.com/paper/2605.21820/) (2026)

## Abstract (arXiv)

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameters need a rapid strategic search through active learning such as Bayesian optimization (BO). However, such high-dimensional experimental physical descriptors are complex and noisy, from which realization of a low-dimensional mathematical scalar metrics or objective functions can be erroneous. Moreover, in traditional purely data-driven autonomous exploration, such objective functions often ignore the subtle variation and key features of the physical descriptors, thereby can fail to discover unknown phenomenon of the material systems. To address this, here we present a proxy-modelled Bayesian optimization (px-BO) via on-the-fly teaming between human and AI agents. Over the loop of BO, instead of defining a mathematical objective function directly from the experimental data, we introduce a voting system on the fly where the new experimental outcome will be compared with existing experiments, and the human agents will choose the preferred samples. These human-guided comparisons are then transformed into a proxy-based objective function via fitting Bradley-Terry (BT) model. Then, to minimize human interaction, this iteratively trained proxy model also acts as an AI agent for future surrogate human votes. Finally, these surrogate votes are periodically validated by human agents, and the corrections are then learned by the proxy model on-the-fly. We demonstrated the performance of the proposed px-BO framework into simulated and BEPS data generated from PTO sample. We find that our approach provided better control of the domain experts for an improved search over traditional data-driven exploration, thus, signifies the importance of human-AI teaming in an accelerated and meaningful material space exploration.